International Journal of

ADVANCED AND APPLIED SCIENCES

EISSN: 2313-3724, Print ISSN: 2313-626X

Frequency: 12
line decor
  
line decor

 Volume 9, Issue 9 (September 2022), Pages: 145-152

----------------------------------------------

 Review Paper

 Recent developments in information extraction approaches from Arabic tweets on social networking sites

 Author(s): Abdullah Ibrahim Abdullah Alzahrani 1, *, Syed Zohaib Javaid Zaidi 2

 Affiliation(s):

 1Department of Computer Science, College of Science and Humanities, Al-Quwayiyah, Shaqra University, Shaqraa, Saudi Arabia
 2Institute of Chemical Engineering and Technology, University of the Punjab, Lahore, Pakistan

  Full Text - PDF          XML

 * Corresponding Author. 

  Corresponding author's ORCID profile: https://orcid.org/0000-0002-4718-7568

 Digital Object Identifier: 

 https://doi.org/10.21833/ijaas.2022.09.018

 Abstract:

Information extraction from Arabic tweets has attracted the attention of researchers due to the huge data accessibility for the swift expansion of social media platforms. With the increasing use of social web applications, information extraction from the various platforms has gained importance for understanding the trending post and events predictions based on those sentiments written by the users on certain news feeds. The Arabic Language is mostly used in Middle Eastern and African countries and most users tweet on social media using the Arabic language, therefore Arabic text classification and sentiment analysis aimed to predict information extraction from social media platforms. This research provides a more detailed critical review of the information extraction presented in the literature focused on using different tools, methods, and techniques like k-NN, support vector machines, Naïve Bayes, and other machine learning tools for the data extraction and processing.

 © 2022 The Authors. Published by IASE.

 This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

 Keywords: Natural language processing, Naïve Bayes, K-NN, Support vector machines

 Article History: Received 5 February 2022, Received in revised form 5 May 2022, Accepted 18 June 2022

 Acknowledgment 

No Acknowledgment.

 Compliance with ethical standards

 Conflict of interest: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

 Citation:

 Alzahrani AIA and Zaidi SZJ (2022). Recent developments in information extraction approaches from Arabic tweets on social networking sites. International Journal of Advanced and Applied Sciences, 9(9): 145-152

 Permanent Link to this page

 Figures

 Fig. 1 Fig. 2 Fig. 3 

 Tables

 Table 1 Table 2

----------------------------------------------    

 References (64)

  1. Abdulla NA, Ahmed NA, Shehab MA, Al-Ayyoub M, Al-Kabi MN, and Al-rifai S (2014). Towards improving the lexicon-based approach for Arabic sentiment analysis. International Journal of Information Technology and Web Engineering, 9(3): 55-71. https://doi.org/10.4018/ijitwe.2014070104   [Google Scholar]
  2. Abdulla NA, Ahmed NA, Shehab MA, and Al-Ayyoub M (2013). Arabic sentiment analysis: Lexicon-based and corpus-based. In the IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies, IEEE, Amman, Jordan: 1-6. https://doi.org/10.1109/AEECT.2013.6716448   [Google Scholar]
  3. Abdullah M and Hadzikadic M (2017). Sentiment analysis on Arabic tweets: Challenges to dissecting the language. In the International Conference on Social Computing and Social Media, Springer, Vancouver, Canada: 191-202. https://doi.org/10.1007/978-3-319-58562-8_15   [Google Scholar]
  4. Abdul-Mageed M, Diab M, and Kübler S (2014). SAMAR: Subjectivity and sentiment analysis for Arabic social media. Computer Speech and Language, 28(1): 20-37. https://doi.org/10.1016/j.csl.2013.03.001   [Google Scholar]
  5. Abend O, Reichart R, and Rappoport A (2009). Unsupervised argument identification for semantic role labeling. In the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP, Association for Computational Linguistics, Suntec, Singapore, 1: 28–36. https://doi.org/10.3115/1687878.1687884   [Google Scholar]
  6. Abo MEM, Raj RG, and Qazi A (2019). A review on Arabic sentiment analysis: State-of-the-art, taxonomy and open research challenges. IEEE Access, 7: 162008-162024. https://doi.org/10.1109/ACCESS.2019.2951530   [Google Scholar]
  7. Abudalfa S and Ahmed M (2017). Survey on target dependent sentiment analysis of micro-blogs in social media. In the 9th IEEE-GCC Conference and Exhibition (GCCCE), IEEE, Manama, Bahrain. https://doi.org/10.1109/IEEEGCC.2017.8448158   [Google Scholar]
  8. Aggarwal CC and Zhai C (2012). A survey of text classification algorithms. In: Aggarwal C and Zhai C (Eds.), Mining text data: 163-222. Springer, Boston, USA. https://doi.org/10.1007/978-1-4614-3223-4_6   [Google Scholar]
  9. Ahmed NA, Shehab MA, Al-Ayyoub M, and Hmeidi I (2015). Scalable multi-label Arabic text classification. In the 6th International Conference on Information and Communication Systems (ICICS), IEEE, Amman, Jordan: 212-217. https://doi.org/10.1109/IACS.2015.7103229   [Google Scholar]
  10. Akaichi J, Dhouioui Z, and Pérez MJLH (2013). Text mining Facebook status updates for sentiment classification. In the 17th International Conference on System Theory, Control and Computing, IEEE, Sinaia, Romania: 640-645. https://doi.org/10.1109/ICSTCC.2013.6689032   [Google Scholar]
  11. Alakrot A (2019). Detection of anti-social behaviour in online communication in Arabic. Ph.D. Dissertation, University of Limerick, Limerick, Ireland.   [Google Scholar]
  12. Al-Ayyoub M, Nuseir A, Alsmearat K, Jararweh Y, and Gupta B (2018). Deep learning for Arabic NLP: A survey. Journal of Computational Science, 26: 522-531. https://doi.org/10.1016/j.jocs.2017.11.011   [Google Scholar]
  13. Al-Horaibi L and Khan MB (2016). Sentiment analysis of Arabic tweets using text mining techniques. In the 1st International Workshop on Pattern Recognition, International Society for Optics and Photonics, Tokyo, Japan, 10011: 288-292. https://doi.org/10.1117/12.2242187   [Google Scholar]
  14. Alhumoud SO, Altuwaijri MI, Albuhairi TM, and Alohaideb WM (2015). Survey on Arabic sentiment analysis in Twitter. International Science Index, 9(1): 364-368.   [Google Scholar]
  15. Al-Laith A and Shahbaz M (2021). Tracking sentiment towards news entities from Arabic news on social media. Future Generation Computer Systems, 118: 467-484. https://doi.org/10.1016/j.future.2021.01.015   [Google Scholar]
  16. Almuqren L, Alzammam A, Alotaibi S, Cristea A, and Alhumoud S (2017). A review on corpus annotation for Arabic sentiment analysis. In the International Conference on Social Computing and Social Media, Springer, Vancouver, Canada: 215-225. https://doi.org/10.1007/978-3-319-58562-8_17   [Google Scholar]
  17. Alomari KM, ElSherif HM, and Shaalan K (2017). Arabic tweets sentimental analysis using machine learning. In the International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, Springer, Arras, France: 602-610. https://doi.org/10.1007/978-3-319-60042-0_66   [Google Scholar]
  18. Al-Osaimi S and Badruddin KM (2014). Role of emotion icons in sentiment classification of Arabic tweets. In the 6th International Conference on Management of Emergent Digital Ecosystems, Association for Computing Machinery, Buraidah, Al Qassim, Saudi Arabia: 167-171. https://doi.org/10.1145/2668260.2668281   [Google Scholar]
  19. Alotaibi S, Mehmood R, and Katib I (2019). Sentiment analysis of Arabic tweets in smart cities: A review of Saudi dialect. In the Fourth International Conference on Fog and Mobile Edge Computing, IEEE, Rome, Italy: 330-335. https://doi.org/10.1109/FMEC.2019.8795331   [Google Scholar]
  20. Al-Radaideh Q (2020). Applications of mining Arabic text: A review. In: Sadollah A and Sinha T (Eds.), Recent trends in computational intelligence: 91-109. BoD–Books on Demand, Norderstedt, Germany. https://doi.org/10.5772/intechopen.91275   [Google Scholar] PMCid:PMC7447403
  21. Alsaedi N and Burnap P (2015). Arabic event detection in social media. In the International Conference on Intelligent Text Processing and Computational Linguistics, Springer, Cairo, Egypt: 384-401. https://doi.org/10.1007/978-3-319-18111-0_29   [Google Scholar]
  22. Alsaleem S (2011). Automated Arabic text categorization using SVM and NB. The International Arab Journal of e-Technology, 2(2): 124-128.   [Google Scholar]
  23. Alsanad A (2018). Arabic topic detection using discriminative multi nominal Naïve Bayes and frequency transforms. In the International Conference on Signal Processing and Machine Learning, Association for Computing Machinery, Shanghai, China: 17-21. https://doi.org/10.1145/3297067.3297095   [Google Scholar]
  24. Alshargi F, Dibas S, Alkhereyf S, Faraj R, Abdulkareem B, Yagi S, and Rambow O (2019). Morphologically annotated corpora for seven Arabic dialects: Taizi, Sanaani, Najdi, Jordanian, Syrian, Iraqi and Moroccan. In the 4th Arabic Natural Language Processing Workshop, Association for Computational Linguistics, Florence, Italy: 137-147. https://doi.org/10.18653/v1/W19-4615   [Google Scholar]
  25. Al-Smadi M, Talafha B, Al-Ayyoub M, and Jararweh Y (2019). Using long short-term memory deep neural networks for aspect-based sentiment analysis of Arabic reviews. International Journal of Machine Learning and Cybernetics, 10(8): 2163-2175. https://doi.org/10.1007/s13042-018-0799-4   [Google Scholar]
  26. Al-Twairesh N, Al-Khalifa H, and Al-Salman A (2014). Subjectivity and sentiment analysis of Arabic: Trends and challenges. In the IEEE/ACS 11th International Conference on Computer Systems and Applications (AICCSA), IEEE, Doha, Qatar: 148-155. https://doi.org/10.1109/AICCSA.2014.7073192   [Google Scholar]
  27. Asur S and Huberman BA (2010). Predicting the future with social media. In the IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, IEEE, Toronto, Canada, 1: 492-499. https://doi.org/10.1109/WI-IAT.2010.63   [Google Scholar]
  28. Atoum JO and Nouman M (2019). Sentiment analysis of Arabic Jordanian dialect tweets. International Journal of Advanced Computer Science and Applications, 10(2): 256-262. https://doi.org/10.14569/IJACSA.2019.0100234   [Google Scholar]
  29. Badaro G, Baly R, Akel R, Fayad L, Khairallah J, Hajj H, and El-Hajj W (2015). A light lexicon-based mobile application for sentiment mining of Arabic tweets. In the 2nd Workshop on Arabic Natural Language Processing, Association for Computational Linguistics, Beijing, China: 18-25. https://doi.org/10.18653/v1/W15-3203   [Google Scholar]
  30. Badaro G, Baly R, Hajj H, El-Hajj W, Shaban KB, Habash N, and Hamdi A (2019). A survey of opinion mining in Arabic: A comprehensive system perspective covering challenges and advances in tools, resources, models, applications, and visualizations. ACM Transactions on Asian and Low-Resource Language Information Processing, 18(3): 1-52. https://doi.org/10.1145/3295662   [Google Scholar]
  31. Baier L, Jöhren F, and Seebacher S (2019). Challenges in the deployment and operation of machine learning in practice. In the 27th European Conference on Information Systems, Stockholm-Uppsala, Sweden: 1-15.   [Google Scholar]
  32. Balahur A (2013). Sentiment analysis in social media texts. In the 4th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, Association for Computational Linguistics, Atlanta, Georgia: 120-128.   [Google Scholar]
  33. Boukil S, Biniz M, El Adnani F, Cherrat L, and El Moutaouakkil AE (2018). Arabic text classification using deep learning technics. International Journal of Grid and Distributed Computing, 11(9): 103-114. https://doi.org/10.14257/ijgdc.2018.11.9.09   [Google Scholar]
  34. Castillo C, Mendoza M, and Poblete B (2011). Information credibility on Twitter. In the 20th International Conference on World Wide Web, Hyderabad, India: 675-684. https://doi.org/10.1145/1963405.1963500   [Google Scholar]
  35. Comunello F and Anzera G (2012). Will the revolution be tweeted? A conceptual framework for understanding the social media and the Arab Spring. Islam and Christian–Muslim Relations, 23(4): 453-470. https://doi.org/10.1080/09596410.2012.712435   [Google Scholar]
  36. Dalal MK and Zaveri MA (2011). Automatic text classification: A technical review. International Journal of Computer Applications, 28(2): 37-40. https://doi.org/10.5120/3358-4633   [Google Scholar]
  37. Dukes K and Habash N (2010). Morphological annotation of Quranic Arabic. In the 7th International Conference on Language Resources and Evaluation, European Language Resources Association, Valletta, Malta: 2530-2536.   [Google Scholar]
  38. Duwairi R and El-Orfali M (2014). A study of the effects of preprocessing strategies on sentiment analysis for Arabic text. Journal of Information Science, 40(4): 501-513. https://doi.org/10.1177/0165551514534143   [Google Scholar]
  39. Duwairi RM, Ahmed NA, and Al-Rifai SY (2015). Detecting sentiment embedded in Arabic social media–A lexicon-based approach. Journal of Intelligent and Fuzzy Systems, 29(1): 107-117. https://doi.org/10.3233/IFS-151574   [Google Scholar]
  40. El-Halees AM (2008). A comparative study on Arabic text classification. Egyptian Computer Science Journal, 30(2): 1-11.   [Google Scholar]
  41. Elhassan R and Ahmed M (2015). Arabic text classification on full word. International Journal of Computer Science and Software Engineering, 4(5): 114-120.   [Google Scholar]
  42. Guellil I, Adeel A, Azouaou F, Chennoufi S, Maafi H, and Hamitouche T (2020). Detecting hate speech against politicians in Arabic community on social media. International Journal of Web Information Systems, 16(3): 295-313. https://doi.org/10.1108/IJWIS-08-2019-0036   [Google Scholar]
  43. Guellil I, Saâdane H, Azouaou F, Gueni B, and Nouvel D (2021). Arabic natural language processing: An overview. Journal of King Saud University-Computer and Information Sciences, 33(5): 497-507. https://doi.org/10.1016/j.jksuci.2019.02.006   [Google Scholar]
  44. Habash N and Sadat F (2006). Arabic preprocessing schemes for statistical machine translation. In the Human Language Technology Conference of the North American Chapter of the ACL, Association for Computational Linguistics, New York, USA: 49-52.   [Google Scholar]
  45. Harrag F, El-Qawasmeh E, and Pichappan P (2009). Improving Arabic text categorization using decision trees. In the 1st International Conference on Networked Digital Technologies, IEEE, Ostrava, Czech Republic: 110–115. https://doi.org/10.1109/NDT.2009.5272214   [Google Scholar]
  46. Husain F (2020). Arabic offensive language detection using machine learning and ensemble machine learning approaches. ArXiv Preprint ArXiv:2005.08946. https://doi.org/10.48550/arXiv.2005.08946    [Google Scholar]
  47. Ismail R, Omer M, Tabir M, Mahadi N, and Amin I (2018). Sentiment analysis for Arabic dialect using supervised learning. In the International Conference on Computer, Control, Electrical, and Electronics Engineering, IEEE, Khartoum, Sudan: 1-6. https://doi.org/10.1109/ICCCEEE.2018.8515862   [Google Scholar] PMCid:PMC5811579
  48. Itani M (2018). Sentiment analysis and resources for informal Arabic text on social media. Ph.D. Dissertation, Sheffield Hallam University, Sheffield, UK. https://doi.org/10.1016/j.procs.2017.10.101   [Google Scholar]
  49. Janasik N, Honkela T, and Bruun H (2009). Text mining in qualitative research: Application of an unsupervised learning method. Organizational Research Methods, 12(3): 436-460. https://doi.org/10.1177/1094428108317202   [Google Scholar]
  50. Jardaneh G, Abdelhaq H, Buzz M, and Johnson D (2019). Classifying Arabic tweets based on credibility using content and user features. In the IEEE Jordan International Joint Conference on Electrical Engineering and Information Technology, IEEE, Amman, Jordan: 596-601. https://doi.org/10.1109/JEEIT.2019.8717386   [Google Scholar]
  51. Joulin A, Grave E, Bojanowski P, and Mikolov T (2016). Bag of tricks for efficient text classification. ArXiv Preprint ArXiv:1607.01759. https://doi.org/10.48550/arXiv.1607.01759   [Google Scholar]
  52. Kaseb GS and Ahmed MF (2016). Arabic sentiment analysis approaches: An analytical survey. International Journal of Scientific and Engineering Research, 7(10): 712-723.   [Google Scholar]
  53. KhosraviNik M and Esposito E (2018). Online hate, digital discourse and critique: Exploring digitally-mediated discursive practices of gender-based hostility. Lodz Papers in Pragmatics, 14(1): 45-68. https://doi.org/10.1515/lpp-2018-0003   [Google Scholar]
  54. Li W (2020). The language of bullying: Social issues on Chinese websites. Aggression and Violent Behavior, 53: 101453. https://doi.org/10.1016/j.avb.2020.101453   [Google Scholar]
  55. Mataoui MH, Zelmati O, and Boumechache M (2016). A proposed lexicon-based sentiment analysis approach for the vernacular Algerian Arabic. Research in Computing Science, 110(1): 55-70. https://doi.org/10.13053/rcs-110-1-5   [Google Scholar]
  56. McNeil K (2018). Tunisian Arabic corpus: Creating a written corpus of an ‘unwritten’ language. Edinburgh University Press, Edinburgh, UK. https://doi.org/10.1515/9780748677382-004   [Google Scholar]
  57. Mohammad SM, Salameh M, and Kiritchenko S (2016). How translation alters sentiment. Journal of Artificial Intelligence Research, 55(1): 95-130. https://doi.org/10.1613/jair.4787   [Google Scholar]
  58. Saad MK and Ashour WM (2010). Arabic text classification using decision trees. In the 12th international workshop on computer science and information technologies CSIT, Moscow, Russia, 2: 75-79.   [Google Scholar]
  59. Salamah JB and Elkhlifi A (2014). Microblogging opinion mining approach for Kuwaiti dialect. In The International Conference on Computing Technology and Information Management, Society of Digital Information and Wireless Communication, Dubai, UAE: 388-396.   [Google Scholar]
  60. Shoukry A and Rafea A (2012). Sentence-level Arabic sentiment analysis. In the International Conference on Collaboration Technologies and Systems, IEEE, Denver, USA: 546-550. https://doi.org/10.1109/CTS.2012.6261103   [Google Scholar]
  61. Traboulsi H (2009). Arabic named entity extraction: A local grammar-based approach. In the International Multiconference on Computer Science and Information Technology, IEEE, Mragowo, Poland: 139-143. https://doi.org/10.1109/IMCSIT.2009.5352809   [Google Scholar]
  62. Yu Y, Duan W, and Cao Q (2013). The impact of social and conventional media on firm equity value: A sentiment analysis approach. Decision Support Systems, 55(4): 919-926. https://doi.org/10.1016/j.dss.2012.12.028   [Google Scholar]
  63. Yue L, Chen W, Li X, Zuo W, and Yin M (2019). A survey of sentiment analysis in social media. Knowledge and Information Systems, 60(2): 617-663. https://doi.org/10.1007/s10115-018-1236-4   [Google Scholar]
  64. Zhang J, Zhan ZH, Lin Y, Chen N, Gong YJ, Zhong JH, and Shi YH (2011). Evolutionary computation meets machine learning: A survey. IEEE Computational Intelligence Magazine, 6(4): 68-75. https://doi.org/10.1109/MCI.2011.942584   [Google Scholar]